tSPM+; a high-performance algorithm for mining transitive sequential patterns from clinical data

• URL: http://arxiv.org/abs/2309.05671v1
• Date: Fri, 8 Sep 2023 17:47:31 GMT
• Title: tSPM+; a high-performance algorithm for mining transitive sequential patterns from clinical data
• Authors: Jonas H\"ugel and Ulrich Sax and Shawn N. Murphy and Hossein Estiri
• Abstract summary: We present the tSPM+ algorithm, a high-performance implementation of the tSPM algorithm, which adds a new dimension by adding the duration to the temporal patterns. We show that the tSPM+ algorithm provides a speed up to factor 980 and a up to 48 fold improvement in memory consumption.
• Score: 5.340674706271038
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing availability of large clinical datasets collected from patients can enable new avenues for computational characterization of complex diseases using different analytic algorithms. One of the promising new methods for extracting knowledge from large clinical datasets involves temporal pattern mining integrated with machine learning workflows. However, mining these temporal patterns is a computational intensive task and has memory repercussions. Current algorithms, such as the temporal sequence pattern mining (tSPM) algorithm, are already providing promising outcomes, but still leave room for optimization. In this paper, we present the tSPM+ algorithm, a high-performance implementation of the tSPM algorithm, which adds a new dimension by adding the duration to the temporal patterns. We show that the tSPM+ algorithm provides a speed up to factor 980 and a up to 48 fold improvement in memory consumption. Moreover, we present a docker container with an R-package, We also provide vignettes for an easy integration into already existing machine learning workflows and use the mined temporal sequences to identify Post COVID-19 patients and their symptoms according to the WHO definition.

Related papers

• QN-Mixer: A Quasi-Newton MLP-Mixer Model for Sparse-View CT Reconstruction [0.0]
  We introduce QN-Mixer, an algorithm based on the quasi-Newton approach. Incept-Mixer is an efficient neural architecture that serves as a non-local regularization term. Our approach intelligently downsamples information, significantly reducing computational requirements.
  arXiv  Detail & Related papers  (2024-02-28T00:20:25Z)
• Custom DNN using Reward Modulated Inverted STDP Learning for Temporal Pattern Recognition [0.0]
  Temporal spike recognition plays a crucial role in various domains, including anomaly detection, keyword spotting and neuroscience. This paper presents a novel algorithm for efficient temporal spike pattern recognition on sparse event series data.
  arXiv  Detail & Related papers  (2023-07-15T18:57:27Z)
• Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP [81.00800920928621]
  We study representation learning in partially observable Markov Decision Processes (POMDPs) We first present an algorithm for decodable POMDPs that combines maximum likelihood estimation (MLE) and optimism in the face of uncertainty (OFU) We then show how to adapt this algorithm to also work in the broader class of $gamma$-observable POMDPs.
  arXiv  Detail & Related papers  (2023-06-21T16:04:03Z)
• TaSPM: Targeted Sequential Pattern Mining [53.234101208024335]
  We propose a generic framework namely TaSPM, based on the fast CM-SPAM algorithm. We also propose several pruning strategies to reduce meaningless operations in mining processes. Experiments show that the novel targeted mining algorithm TaSPM can achieve faster running time and less memory consumption.
  arXiv  Detail & Related papers  (2022-02-26T17:49:47Z)
• Bioinspired Cortex-based Fast Codebook Generation [0.09449650062296822]
  We introduce a feature extraction method inspired by sensory cortical networks in the brain. Dubbed as bioinspired cortex, the algorithm provides convergence to features from streaming signals with superior computational efficiency. We show herein the superior performance of the cortex model in clustering and vector quantization.
  arXiv  Detail & Related papers  (2022-01-28T18:37:43Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
  Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
  arXiv  Detail & Related papers  (2021-06-05T01:31:51Z)
• Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information [52.635997570873194]
  This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information. The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
  arXiv  Detail & Related papers  (2021-01-12T20:08:18Z)
• Fast Reinforcement Learning with Incremental Gaussian Mixture Models [0.0]
  An online and incremental algorithm capable of learning from a single pass through data, called Incremental Gaussian Mixture Network (IGMN), was employed as a sample-efficient function approximator for the joint state and Q-values space. Results are analyzed to explain the properties of the obtained algorithm, and it is observed that the use of the IGMN function approximator brings some important advantages to reinforcement learning in relation to conventional neural networks trained by gradient descent methods.
  arXiv  Detail & Related papers  (2020-11-02T03:18:15Z)
• Improving a State-of-the-Art Heuristic for the Minimum Latency Problem with Data Mining [69.00394670035747]
  Hybrid metaheuristics have become a trend in operations research. A successful example combines the Greedy Randomized Adaptive Search Procedures (GRASP) and data mining techniques.
  arXiv  Detail & Related papers  (2019-08-28T13:12:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.